Testcross Research Articles

Comparing modeled predictions of coral reef diversity along a latitudinal gradient in Mozambique

IntroductionPredictive models based on environmental proxy data are being used to predict biodiversity on large and even global scales. Yet, some of the underlying assumptions about the relationship between proxy variables and predictions require investigations and testing the consequences of using model alternatives, data sources, variables choices, and scales, extent, and overlap among the predictions. Mozambican coral reefs provide a good case study to test these assumptions given the paucity of field data, its long coastline, and transitions from tropical to temperate environments.MethodsThree modelling formulations and 5 specific models were made using satellite and shipboard measurements and extensive fish and corals field data to test their performance in predicting numbers of fish species and coral taxa from field data. Model predictions were mapped for the 1180 ~6.25 km2 Mozambican coral reef cells. Predictions were made and mapped 1) based on ~1000 field sites in the Western Indian Ocean (WIO) faunal province model, 2) using environmental variable selected in the WIO model (WIOMOD) but trained only with Mozambican field data (<113 sites), and 3) using only Mozambican environmental and field data and standard variable redundancy and selection procedures.Results and discussionTraining and testing cross validation of models indicated modest predictive ability (R2~0.42-0.56%) and reasonable transferability. Consequently, there was unexplained variation likely due to small-scale environmental variability finer than the mapped cell scale. Differences between model predictions were caused by different variable rankings and response relationship. For example, the Mozambique-only model predicted more fish but fewer coral taxa, a larger role of water quality and sediments, habitats, and temperature variation, and a lesser role of human influence than the WIOMOD. Therefore, differences between models indicate that large scale models (i.e. provincial or global) can contribute to understanding gross patterns but miss important local environmental and human drivers in transitional environments. Nevertheless, 79% of the fish and 88% of coral taxa cell-level predictions of taxonomic diversity had standardized coefficients of variations of <10%.

Strategic genetic amelioration of quality protein maize (QPM) germplasm and its utilization in hybrid breeding

Abstract Use of diverse germplasm for generating heterotic hybrids is the foremost requirement in maize. The present study was conducted by using a diverse set of inbred lines and the line × tester method was applied to identify best performing lines and to group QPM inbred lines into different heterotic groups. The test crosses, developed by following line (66) × tester (CML 161 and CML 165) mating design, were evaluated during winter 2013, rainy 2014 and 2015 seasons at Begusarai and Ludhiana, respectively. Based on the specific combining ability, the lines were categorized into two heterotic groups. Out of 66 novel inbreds, 18 lines with significant SCA with CML165 were classified in group A, 16 inbreds with significant SCA with CML161 were classified in group B and 20 inbreds with significant GCA were classified in group (AB). Nine inbred lines were selected based on their positive GCA values and pedigree crosses were developed in rainy season in 2017. Three crosses were made in heterotic group A and four crosses were in group B for synthesizing new inbred lines by using pedigree method. Heterotic grouping based inbred evaluation trial and biochemical analysis were carried out to estimate per se yield potential of developed lines and to estimate tryptophan content. QIL-4-2491 (Group-A) and QIL-4-2401 (Group-B) were the top yielders. A total of 25 crosses were made among the heterotic groups (A and B) by using 22 lines from groups A and B and three best performing hybrids were identified.

Selection of Maize Hybrids Resulting from Line × Tester Crossing Tolerant to Drought Stress

Efforts to enhance maize productivity in regions like Madura Island, Indonesia, plagued by low yields due to drought stress, necessitate the development of resilient and high-yielding maize hybrids. Employing the line x tester method represents a promising approach to breed varieties tolerant to drought stress. This study aims to identify drought-tolerant maize hybrids derived from line x tester crosses. The research utilized a Randomized Complete Block Design (RCBD) with 40 genotypes replicated three times, totaling 120 experimental units. Drought stress was imposed following the CIMMYT method, wherein irrigation was provided at field capacity from 0 to 40 days after planting (DAP) at 10-day intervals, while drought stress commenced from 50 DAP until harvest. Under optimal conditions, irrigation occurred every 10 days until 80 DAP. Key observation parameters included plant height, days to 50% tasselling, harvest age, ear height, ear length, ear diameter, kernel weight per plant, 1000-kernel weight, and production per hectare. Results indicated several adaptive hybrid maize genotypes for both normal and drought conditions, as discerned by the Stress Tolerance Index (STI), including G2, G12, G13, G14, and G15. Moreover, hybrids G2, G18, G19, G21, and G24 exhibited notable drought tolerance based on the Stress Susceptibility Index (SSI). These findings underscore the potential of employing the line x tester method in breeding resilient maize hybrids tailored to mitigate the adverse impacts of drought stress, thereby fostering agricultural sustainability in resource-constrained environments like Madura Island.

M5c-iDeep: 5-Methylcytosine sites identification through deep learning

5-Methylcytosine (m5c) is a modified cytosine base which is formed as the result of addition of methyl group added at position 5 of carbon. This modification is one of the most common PTM that used to occur in almost all types of RNA. The conventional laboratory methods do not provide quick reliable identification of m5c sites. However, the sequence data readiness has made it feasible to develop computationally intelligent models that optimize the identification process for accuracy and robustness. The present research focused on the development of in-silico methods built using deep learning models. The encoded data was then fed into deep learning models, which included gated recurrent unit (GRU), long short-term memory (LSTM), and bi-directional LSTM (Bi-LSTM). After that, the models were subjected to a rigorous evaluation process that included both independent set testing and 10-fold cross validation. The results revealed that LSTM-based model, m5c-iDeep, outperformed revealing 99.9 % accuracy while comparing with existing m5c predictors. In order to facilitate researchers, m5c-iDeep was also deployed on a web-based server which is accessible at https://taseersuleman-m5c-ideep-m5c-ideep.streamlit.app/.

Development of Hybrids with Promising Restorers under Test Cross Nursery Study in Rice (Oryza Sativa L.)

Development of Hybrids with Promising Restorers under Test Cross Nursery Study in Rice (Oryza Sativa L.)

Character Association and Path Analysis for Yield and its Attributing Traits in Urdbean Genotypes and its Triple Test Cross F1 Hybrids

This research delves into the association and path coefficient analysis between yield and its attributing traits among 111 diverse lines of urdbean. 27 lines, three testers, and Eighty-one F1 hybrids were grown in a Randomized Block Design (RBD) with three replications during Kharif 2023 at the Crop Experimental Research Farm of Chandra Shekhar Azad University of Agriculture and Technology, Kalyanpur, Kanpur (U.P.). The analysis of variance for parents (27 lines and three testers) and their 81 triple test cross hybrids indicated highly significant genotypic differences across all 12 quantitative traits under study viz., days to 50% flowering (DFF), days to 75% maturity (DM), plant height (cm) (PH), number of primary branches per plant (NPB), pods per plant (NPP), pod length (cm) (PL), seeds per pod (NSP), biological yield per plant (g) (BYPP), seed yield per plant (g) (SYPP), harvest index (%),(HI), 100-seed weight (HSW), and protein content (%) (PC). All the traits studied showed a significant positive correlation with seed yield, except for days to 50% flowering, and days to 75 % maturity at phenotypic and genotypic levels. Path analysis revealed that all the traits had high direct positive effects on seed yield except for days to 50% flowering and days to 75 % maturity at phenotypic and genotypic levels. Therefore, these traits should be the primary focus in black gram breeding programs aimed at yield improvement.

Exploring Genetic Variability Parameters for Yield and Quality Traits in Urdbean Genotypes and Its Triple Test Cross F1 Hybrids

Exploring Genetic Variability Parameters for Yield and Quality Traits in Urdbean Genotypes and Its Triple Test Cross F1 Hybrids

Identification of efficient inbred testers to strengthen established heterotic groups in pearl millet (Pennisetum glaucum [L.] R. Br)

AbstractPearl millet has two major heterotic groups, HGB (Heterotic Group B) and HGR (Heterotic Group R), each with unique genetic backgrounds and traits. Leveraging appropriate genetic resources is crucial for enhancing the potential of these heterotic groups in hybrid breeding programmes. Testers are essential in this context, determining the combining ability of new breeding lines and accurately assessing the performance and potential of parental lines in hybrid combinations. Two sets of test crosses were produced using line × tester mating design: Set 1 with six HGB testers crossed with new restorer lines and Set 2 with six HGR testers crossed with new seed parental lines, evaluated at multiple locations in India. Testers were evaluated for their effectiveness using multiple criteria, such as rank correlation analysis for line performance in hybrid combinations, discrimination ability, general combining ability, per se performance and Biplot analysis. These assessments determined their relative ranking, with HGB1‐T3, HGB2‐T3, HGR1‐T1 and HGR2‐T1 identified as efficient testers. The genetic analysis helps identify efficient testers for selecting superior genotypes, thereby enhancing heterotic group performance by incorporating appropriate new breeding material.

Utilizing genomic prediction to boost hybrid performance in a sweet corn breeding program.

Sweet corn breeding programs, like field corn, focus on the development of elite inbred lines to produce commercial hybrids. For this reason, genomic selection models can help the in silico prediction of hybrid crosses from the elite lines, which is hypothesized to improve the test cross scheme, leading to higher genetic gain in a breeding program. This study aimed to explore the potential of implementing genomic selection in a sweet corn breeding program through hybrid prediction in a within-site across-year and across-site framework. A total of 506 hybrids were evaluated in six environments (California, Florida, and Wisconsin, in the years 2020 and 2021). A total of 20 traits from three different groups were measured (plant-, ear-, and flavor-related traits) across the six environments. Eight statistical models were considered for prediction, as the combination of two genomic prediction models (GBLUP and RKHS) with two different kernels (additive and additive + dominance), and in a single- and multi-trait framework. Also, three different cross-validation schemes were tested (CV1, CV0, and CV00). The different models were then compared based on the correlation between the estimated breeding values/total genetic values and phenotypic measurements. Overall, heritabilities and correlations varied among the traits. The models implemented showed good accuracies for trait prediction. The GBLUP implementation outperformed RKHS in all cross-validation schemes and models. Models with additive plus dominance kernels presented a slight improvement over the models with only additive kernels for some of the models examined. In addition, models for within-site across-year and across-site performed better in the CV0 than the CV00 scheme, on average. Hence, GBLUP should be considered as a standard model for sweet corn hybrid prediction. In addition, we found that the implementation of genomic prediction in a sweet corn breeding program presented reliable results, which can improve the testcross stage by identifying the top candidates that will reach advanced field-testing stages.

Using triple test cross analysis to estimate genetic components and genetic correlation for some quantitative traits in cotton

Using triple test cross analysis to estimate genetic components and genetic correlation for some quantitative traits in cotton

Testcross performance of Striga-resistant maize inbred lines and testers with varying levels of Striga reaction

BackgroundUsing a desirable tester is considered one method used to maximise genetic differences among test crosses derived from new inbred lines and improves the overall performance of maize. Thus, this study aimed to evaluate the potency of the tester with varying levels of resistance to Striga hermonthica in determining the testcross performance of the hybrids for Striga resistance and yield-related traits.MethodThe experiment was conducted with these test crosses and two standard checks (susceptible and tolerant) for different Striga resistance and agronomic traits during the 2018 cropping season in Abuja and Mokwa, Nigeria. The experiment was laid out in a 23 × 4 alpha-lattice design with two replications in each location. Field evaluation data was collected from Striga resistance and yield-related traits to estimate the performance of test crosses. Analysis of variance was conducted to determine the variance of the testcross performance.ResultsThere were significant differences among test crosses for days to silking, days to pollen shedding, ear at harvest, ear aspect, ear per plant, grain yield, Striga damage rating at 8 and 10 weeks after planting (WAP), and Striga count at 8 and 10 WAP. Variations among test crosses were always higher than the corresponding variations due to the interaction between test crosses and the environment for all traits.ConclusionThe inbred lines with low yield reduction crossed with different testers under Striga infested were recorded. These inbreds should be used to develop high-yielding hybrids and synthetics with elevated levels of Striga resistance to improve the maize breeding program.

Effects of fluorene-9-bisphenol exposure on anxiety-like and social behavior in mice and protective potential of exogenous melatonin.

Fluorene-9-bisphenol (BHPF) is widely used in the manufacture of plastic products and potentially disrupts several physiological processes, but its biological effects on social behavior remain unknown. In this study, we investigated the effects of BHPF exposure on anxiety-like and social behavior in female mice and the potential mechanisms, thereby proposing a potential therapy strategy. We exposed female Balb/c mice to BHPF by oral gavage at different doses (0.5, 50 mg/kg bw/2-day) for 28 days, which were found BHPF (50 mg/kg) exposure affected motor activity in the open field test (OFT) and elevated cross maze (EPM), resulting in anxiety-like behaviors, as well as abnormal social behavioral deficits in the Social Interaction Test (SIT). Analysis of histopathological staining results showed that BHPF exposure caused damage to hippocampal neurons in the CA1/CA3/DG region and decreased Nissl pyramidal neurons in the CA1/CA3 regions of the hippocampus, as well as a decrease in parvalbumin neuron expression. In addition, BHPF exposure upregulated the expression of excitatory and inhibitory (E/I) vesicle transporter genes (Vglut1, Vglut2, VGAT, GAD67, Gabra) and axon growth gene (Dcc) in the mouse hippocampus. Interestingly, behavioral disturbances and E/I balance could be alleviated by exogenous melatonin (15 mg/kg bw/2-day) therapy. Our findings suggest that exogenous melatonin may be a potential therapy with protective potential for ameliorating or preventing BHPF-induced hippocampal neuronal damage and behavioral disturbances. This study provided new insight into the neurotoxicological effects on organisms exposed to endocrine-disrupting chemicals and aroused our vigilance in current environmental safety about chemical use.

Disease Reaction Studies for Turcicum Leaf Blight (Exserohilum Turcicum) Disease in Maize (Zea Mays L.) Under Artificial Epiphytotic Conditions

Maize is widely cultivated around the globe under varied climatic conditions. India is one of the major maize producing countries and stands fourth in terms of production. Turcicum leaf blight (TLB) disease caused by the pathogen, Exserohilum turcicum is one of the major diseases of maize causing varying extent of grain yield loss from 15 to 30%. To overcome the losses due to the disease, resistant cultivars needs to be developed. Hence, the identification of resistant sources is important in order to develop TLB resistant hybrids. Present investigation was conducted to identify TLB resistant inbreds and hybrids. Simplified Triple Test Cross (STTC) design was employed to develop 96 hybrids using twenty inbred lines. Further the hybrids and inbred lines along with two checks (Resistant –NAI 137; Susceptible – CM 202) were evaluated for TLB disease reaction under artificial epiphytotic condition during rabi 2019 in simple lattice design with two replications. Scoring was done from 60 to 80 days after sowing based on 1-9 TLB disease scale. Among the inbred lines V-86, MAI-295, V-70, MAI-214, V-85, 40375, V-72, V-39, 40061 were resistant and MAI-288, V-24 were susceptible. Among hybrids, 36 were resistant, 44 were moderately resistant. 14 were moderately susceptible and two hybrids were susceptible to TLB disease. Identified TLB resistant hybrids needs to be further evaluated to test their performance and stability across locations. These resistant sources can be used to transfer TLB disease resistance.

Classification of new germplasm into existing heterotic groups of pearl millet [Pennisetum glaucum (L.) R. Br.

AbstractThe study assigned new germplasm, which includes populations and inbreds, to established heterotic groups using various approaches to broaden the existing genetic base while maintaining the heterotic pattern in pearl millet [Pennisetum glaucum (L.) R. Br.]. It utilized 13 pearl millet populations of African and Asian origins and 24 new inbred parents from ICRISAT's breeding program. Testers, both inbred and composite, were employed to categorize these materials into heterotic groups. Different sets of line × tester crosses were generated and evaluated during the rainy season at multiple locations in India. The pearl millet populations were assigned to heterotic group B (seed parental groups) (HGB1 and HGB2) and heterotic group R (pollinator parental groups) (HGR1 and HGR2) based on general combining ability (GCA) and specific combining ability effects. Composite testers were found to be more effective for the heterotic grouping of pearl millet populations. New inbred lines were classified into HGB and HGR based on GCA and hybrid performance using opposite heterotic group testers and also using genetic similarity obtained from genotype‐by‐sequencing data.The new germplasm classified into heterotic groups will help enhance the genetic gain for the long‐term success of pearl millet hybrid breeding programs.

M1A-Ensem: accurate identification of 1-methyladenosine sites through ensemble models

Background1-methyladenosine (m1A) is a variant of methyladenosine that holds a methyl substituent in the 1st position having a prominent role in RNA stability and human metabolites.ObjectiveTraditional approaches, such as mass spectrometry and site-directed mutagenesis, proved to be time-consuming and complicated.MethodologyThe present research focused on the identification of m1A sites within RNA sequences using novel feature development mechanisms. The obtained features were used to train the ensemble models, including blending, boosting, and bagging. Independent testing and k-fold cross validation were then performed on the trained ensemble models.ResultsThe proposed model outperformed the preexisting predictors and revealed optimized scores based on major accuracy metrics.ConclusionFor research purpose, a user-friendly webserver of the proposed model can be accessed through https://taseersuleman-m1a-ensem1.streamlit.app/.

Bark extract of Chaetocarpus castanocarpus (Roxb.) exhibits potent sedative, anxiolytic, and antidepressant effects through an in vivo approach in Swiss albino mice.

Standard phytochemical investigations were performed to identify the secondary metabolites in the methanol extract of Chaetocarpus castanocarpus bark (MECC) and investigate the neuropharmacological potential of MECC in Swiss albino mice. Swiss albino mice were used in the forced swimming test (FST) and tail suspension test (TST) to evaluate the antidepressant effect of MECC. Also, the hole board test (HBT) and elevated plus maze (EPM) were conducted to examine anxiolytic activities. In contrast, the open field test (OFT) and hole cross test (HCT) were employed to evaluate sleeping disorders. Alkaloids, glycosides, flavonoids, terpenoids, coumarins, and tannins are only a few secondary metabolites identified in MECC by qualitative and quantitative phytochemical investigations. The oral administration of MECC considerably shortened the immobility duration during FST and TST. Encouraging dose-dependent anxiolytic effects were also observed in all relevant experiments compared to the control. Additionally, during the OFT and HCT assessment, a noteworthy decline in the locomotor activities of the experimental animals was observed. The results of this investigation suggest that the Chaetocarpus castanocarpus bark is a possible source of therapeutic candidates for treating neurological disorders.

Research on Genotype Markers for Plant Height and Assisted Breeding of Key Sorghum Resources in China.

Dwarfing and the selection of optimal plant types constitute the primary focus of sorghum breeding. However, the lack of clarity regarding the gene types associated with plant height genes Dw1-Dw4 in the primary breeding materials has led to increased plant heights in improved offspring of the same plant height type, resulting in unsatisfactory morphological traits. This study aimed to elucidate the gene types related to plant height in breeding materials, validate the regulatory mechanisms, and establish a material improvement system. The goal was to achieve molecular-marker-assisted dwarf breeding through the detection of plant height genes and the test cross verification of main Chinese sorghum materials. Using 38 main male sterile lines and 57 main restorer lines of grain sorghum as materials, three plant height genes were detected and classified. Ninety-five F1 generation hybrids of these materials, along with typical materials, were measured at the wax maturity stage. Test cross results demonstrated that the variation in dw1-dw3 genes in the breeding materials significantly influenced the plant height of hybrid offspring. The main male sterile lines in Chinese sorghum predominantly exhibited the "three-dwarf" type of Kafir and its improved lines, characterized by the genotype (Dw1-Dw2-dw3-dw4). On the other hand, restorer lines mainly showcased the improved "two-dwarf" (Dw1-Dw2-dw3-dw4) genotype of the Kaoliang/Caudatum subspecies, along with the "three-dwarf" type of some Kafir and its improved lines. The test materials predominantly contained dw3 genes, with relatively fewer dw1 genes in the restorer lines. The primary restorer materials lacked the dw2 gene, and dw2 significantly influenced plant type. The increased plant height in improved offspring of the same plant height type material was attributed to differences in gene types. Therefore, the enhancement of plant height in breeding materials should prioritize the use of different methods in conjunction with Dw1 and Dw2 classification.

Factors influencing the variation in canopy light extinction coefficient (k) among pisifera parents of two oil palm origins

Summary The canopy light extinction coefficient (k) is defined as the exponential decline in the amount of light passing through the leaf layers as a function of leaf area index (LAI). This definition is standard in oil palm breeding trials and models of canopy photosynthesis, where k is sometimes assumed to have a fixed value. The present experiment aims to validate the alleged constancy of k. Therefore, k was inferred from the fractional transmission of photosynthetically active radiation (PAR) and LAI, as obtained from dura x pisifera test crosses of Nigeria and Ghana pisifera origins. The palms were planted at two densities (135 and 160 palms ha−1) in North Sumatra in 2010. At the age of 7.5 years after planting, the area of newly opened leaves approached a maximum. Transmission of PAR remained very low and was only weakly related to k. By contrast, LAI exerted a strong negative effect on k, which generated, under both densities, considerable differences in k between both origins and among pisifera within an origin. The assumption of applying a fixed k value for a certain genotype or palm density, as obtained during leaf expansion at closed canopy, may therefore not be realistic. The present study suggests that the relationship of k with LAI over time merits further investigation, starting just before canopy closure.

CRPU-NET: a deep learning model based semantic segmentation for the detection of colorectal polyp in lower gastrointestinal tract

Purpose. The objectives of the proposed work are twofold. Firstly, to develop a specialized light weight CRPU-Net for the segmentation of polyps in colonoscopy images. Secondly, to conduct a comparative analysis of the performance of CRPU-Net with implemented state-of-the-art models. Methods. We have utilized two distinct colonoscopy image datasets such as CVC-ColonDB and CVC-ClinicDB. This paper introduces the CRPU-Net, a novel approach for the automated segmentation of polyps in colorectal regions. A comprehensive series of experiments was conducted using the CRPU-Net, and its performance was compared with that of state-of-the-art models such as VGG16, VGG19, U-Net and ResUnet++. Additional analysis such as ablation study, generalizability test and 5-fold cross validation were performed. Results. The CRPU-Net achieved the segmentation accuracy of 96.42% compared to state-of-the-art model like ResUnet++ (90.91%). The Jaccard coefficient of 93.96% and Dice coefficient of 95.77% was obtained by comparing the segmentation performance of the CRPU-Net with ground truth. Conclusion. The CRPU-Net exhibits outstanding performance in Segmentation of polyp and holds promise for integration into colonoscopy devices enabling efficient operation.

An ensemble-based deep learning model for detection of mutation causing cutaneous melanoma

When the mutation affects the melanocytes of the body, a condition called melanoma results which is one of the deadliest skin cancers. Early detection of cutaneous melanoma is vital for raising the chances of survival. Melanoma can be due to inherited defective genes or due to environmental factors such as excessive sun exposure. The accuracy of the state-of-the-art computer-aided diagnosis systems is unsatisfactory. Moreover, the major drawback of medical imaging is the shortage of labeled data. Generalized classifiers are required to diagnose melanoma to avoid overfitting the dataset. To address these issues, blending ensemble-based deep learning (BEDLM-CMS) model is proposed to detect mutation of cutaneous melanoma by integrating long short-term memory (LSTM), Bi-directional LSTM (BLSTM) and gated recurrent unit (GRU) architectures. The dataset used in the proposed study contains 2608 human samples and 6778 mutations in total along with 75 types of genes. The most prominent genes that function as biomarkers for early diagnosis and prognosis are utilized. Multiple extraction techniques are used in this study to extract the most-prominent features. Afterwards, we applied different DL models optimized through grid search technique to diagnose melanoma. The validity of the results is confirmed using several techniques, including tenfold cross validation (10-FCVT), independent set (IST), and self-consistency (SCT). For validation of the results multiple metrics are used which include accuracy, specificity, sensitivity, and Matthews’s correlation coefficient. BEDLM gives the highest accuracy of 97% in the independent set test whereas in self-consistency test and tenfold cross validation test it gives 94% and 93% accuracy, respectively. Accuracy of in self-consistency test, independent set test, and tenfold cross validation test is LSTM (96%, 94%, 92%), GRU (93%, 94%, 91%), and BLSTM (99%, 98%, 93%), respectively. The findings demonstrate that the proposed BEDLM-CMS can be used effectively applied for early diagnosis and treatment efficacy evaluation of cutaneous melanoma.